#!/usr/bin/env python3
# -*- coding: utf-8 -*-

"""
工业低碳转型模块

功能：
1. 工业过程碳排放分析与优化
2. 基于混合模型(符号逻辑+深度学习)的工艺改进建议生成
3. 碳足迹追踪与报告
4. 可解释性分析(SHAP)
"""

import torch
import numpy as np
import shap
from typing import Dict, Any
from transformers import GPT2LMHeadModel, GPT2Tokenizer

class RuleEngine:
    """符号逻辑规则引擎"""
    
    def __init__(self):
        self.rules = {
            'temperature': lambda x: x > 100,
            'pressure': lambda x: x < 2.5,
            'energy_source': lambda x: x in ['renewable', 'electric']
        }
    
    def apply_rules(self, process_data: Dict[str, Any]) -> str:
        """应用业务规则生成建议"""
        suggestions = []
        for param, value in process_data.items():
            if param in self.rules and not self.rules[param](value):
                suggestions.append(f"调整{param}至合规范围")
        return "。".join(suggestions) if suggestions else "符合所有工艺规则"

class SHAPExplainer:
    """SHAP可解释性分析"""
    
    def __init__(self, model):
        self.explainer = shap.Explainer(model)
    
    def explain(self, input_data):
        """解释模型预测"""
        shap_values = self.explainer(input_data)
        return {
            'feature_importance': shap_values.abs.mean(0).tolist(),
            'expected_value': shap_values.base_values.mean()
        }

class HybridModelDecorator:
    """混合模型装饰器"""
    
    def __init__(self, dl_model, rule_engine):
        self.dl_model = dl_model
        self.rule_engine = rule_engine
    
    def predict(self, process_data):
        """结合规则和深度学习进行预测"""
        rule_based = self.rule_engine.apply_rules(process_data)
        dl_based = self.dl_model(process_data)
        return f"规则建议: {rule_based}\nAI建议: {dl_based}"

class IndustrialLowCarbon:
    """工业低碳转型核心类"""
    
    def __init__(self, hybrid_mode=True):
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self.tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
        self.tokenizer.pad_token = self.tokenizer.eos_token
        self.model = GPT2LMHeadModel.from_pretrained("gpt2").to(self.device)
        self.rule_engine = RuleEngine()
        self.shap_explainer = SHAPExplainer(self.model)
        self.hybrid_mode = hybrid_mode
        
    def optimize_process(self, input_data, explain=False):
        """
        生成工艺优化建议
        
        参数:
            input_data: 工艺参数字典或文本描述
            explain: 是否返回解释性分析
            
        返回:
            优化建议字符串或字典(当explain=True时)
        """
        if isinstance(input_data, dict):
            # 结构化数据模式
            if self.hybrid_mode:
                hybrid_model = HybridModelDecorator(
                    lambda x: self._generate_text(str(x)),
                    self.rule_engine
                )
                result = hybrid_model.predict(input_data)
            else:
                result = self._generate_text(str(input_data))
                
            if explain:
                explanation = self.shap_explainer.explain(input_data)
                return {
                    'recommendation': result,
                    'explanation': explanation
                }
            return result
        else:
            # 纯文本模式(向后兼容)
            return self._generate_text(input_data)
            
    def _generate_text(self, text_input):
        """原始文本生成方法"""
        prompt = f"给定以下工业工艺数据: {text_input}。请提供降低碳排放的优化建议:"
        inputs = self.tokenizer(prompt, return_tensors="pt").to(self.device)
        outputs = self.model.generate(**inputs, max_length=200)
        return self.tokenizer.decode(outputs[0], skip_special_tokens=True)
    
    def predict_emission(self, historical_data):
        """
        预测未来碳排放趋势
        
        参数:
            historical_data: 历史碳排放数据 (numpy数组)
            
        返回:
            预测结果 (numpy数组)
        """
        # 示例: 简单的线性回归预测
        x = np.arange(len(historical_data))
        coeffs = np.polyfit(x, historical_data, 1)
        future = np.arange(len(historical_data), len(historical_data)+12)
        return coeffs[0] * future + coeffs[1]
    
    def generate_report(self):
        """生成碳减排报告"""
        return "工业低碳转型进展报告: ..."